جست‌وجوی استاد و پژوهشگر

Miguel Bessaمشاهده پروفایل

دانشیار

• computational mechanics and materials science
• development of numerical methods
• machine learning and optimization
• +۱ مورد دیگر

Miguel Bessa is an Associate Professor of Engineering at Brown University's School of Engineering. His research focuses on computational mechanics, materials science, machine learning, and multi-scale modeling. He develops advanced numerical methods and integrates machine learning techniques to optimize materials and structures. Notable projects include topology optimization for energy absorption, neural network-based material models, and frameworks for predictive materials modeling. His work often bridges theory and application, leveraging data-driven approaches to solve complex engineering challenges. His office is located at Barus & Holley 731. He has contributed to software tools like f3dasm and CRATE, advancing data-driven design and simulation. Bessa’s research emphasizes interdisciplinary collaboration, combining computational methods with experimental validation to enhance material performance and structural reliability. Recent work highlights include scalable photonic crystal designs, fracture-resistant topology optimization, and meta-learning frameworks for knowledge infusion. His methodologies address uncertainty quantification, multi-fidelity modeling, and continual learning, reflecting a commitment to advancing both fundamental science and practical engineering solutions.

Peter Kernمشاهده پروفایل

استاد

• Stochastic Processes
• Probability Theory
• Self-Similarity
• +۳ مورد دیگر

Prof. Dr. Peter Kern is a Chair of Mathematical Statistics and Probability Theory at the Mathematical Institute of Heinrich Heine University Düsseldorf (HHU). His research focuses on stochastic processes, self-similarity, operator scaling, and fractal geometry, with significant contributions to limit theorems and fractional calculus. Prof. Kern has co-authored numerous peer-reviewed publications on topics such as semi-fractional diffusion equations, dilatively stable processes, and the Hausdorff dimension of Lévy processes. His recent work includes collaborations with researchers from HHU, Bochum, and Siegen on digital educational resources for stochastics, funded by the Ministry of Culture and Science of North Rhine-Westphalia. In his field, Prof. Kern explores connections between operator stable laws, semistable distributions, and their applications to random walks, fractal path properties, and stochastic modeling. He is actively involved in developing interactive learning tools and video tutorials for mathematics education, targeting students in natural and engineering sciences. Prof. Kern's funded projects include "Digital Materials in Stochastics Teaching" (OERContent.nrw, 10/2020–09/2022), "Dimension of Multivariate Self-Similar Processes" (DFG, 2013–2016), and "Dilatively Stable Processes" (DAAD-MÖB, 2014–2015).

Scott DeLoachمشاهده پروفایل

استاد

• Multiagent Systems
• Distributed Systems
• Software Engineering
• +۵ مورد دیگر

Scott DeLoach is a Professor and Department Head in the Department of Computer Science at Kansas State University's College of Engineering. He holds a B.S. in Computer Engineering from Iowa State University (1982), and M.S. (1987) and Ph.D. (1996) degrees in Computer Engineering from the Air Force Institute of Technology. Before joining K-State in 2001, he served 20 years in the U.S. Air Force, working at the Aeronautical Systems Division, 544th Strategic Intelligence Wing, Air Force Research Laboratory, and Air Force Institute of Technology. He became interim department head in 2014 and permanent head in 2016, serving until his scheduled retirement in June 2025. His research applies software engineering methodologies to intelligent, adaptive multiagent and distributed systems, with current focus areas including: Runtime/design-time models for smart grid and power distribution systems Adaptive network security using runtime models Formal specification of agent-oriented systems Self-tuning autonomous systems through organizational policies DeLoach's publications (20+ journal articles, 75+ conference papers) predominantly explore multiagent systems, adaptive security, and formal verification methods, demonstrating consistent focus on theoretical foundations with practical applications in critical infrastructure. Awards & Honors: National Science Foundation CAREER Award (2004) K-State College of Engineering Frankenhoff Outstanding Research Award (2011) K-State Outstanding Department Head Award (2021) He has advised over 30 graduate students, including 7 Ph.D. graduates, primarily through the Multi-Agent & Cooperative Robotics (MACR) Laboratory . His leadership includes editorial duties for the International Journal of Agent-Oriented Software Engineering (since 2005) and participation in numerous program committees.

Ayaan M. Kazerouniمشاهده پروفایل

استادیار

• Computing Education
• Software Engineering
• Software Testing
• +۳ مورد دیگر

Ayaan M. Kazerouni is an Assistant Professor in the Computer Science and Software Engineering (CSSE) Department at California Polytechnic State University (Cal Poly). Her research focuses on computing education, software engineering, and software testing, with a particular emphasis on socially responsible computing to promote equity in STEM education. She holds a PhD in Computer Science from Virginia Tech (2020) and a BS in Computer Science from the University of West Georgia (2015). Her work addresses challenges in student retention among underrepresented groups, particularly Latinx students at Hispanic-Serving Institutions (HSIs), through curriculum redesign and pedagogical interventions. She also leads initiatives like Hack4Impact and the CSSE student tutoring center, fostering community engagement and academic support. Key research contributions include studies on debugging practices, incremental testing strategies, and the impact of milestones on student procrastination. Her work has been recognized with awards such as 1st Place in the ACM SIGCSE Student Research Competition (2018) and the 2nd Best Research Paper at SIGCSE (2019). She collaborates with institutions nationwide through NSF-funded initiatives like the Socially Responsible Computing Alliance, aiming to broaden participation in computing. Her research leverages data-centric approaches, including IDE logging (ProgSnap2) and repository mining, to assess student development processes. She actively contributes to open-source tools like DevEventTracker and incremental-testing frameworks, advancing educational analytics and software engineering practices.

Ole Lübkeمشاهده پروفایل

پژوهشگر

• Real-Time Systems
• Formal Methods
• Maximum Satisfiability

Ole Lübke is a Researcher at the Institute for Software Systems at Technische Universität Hamburg. He holds a Master's degree in Computer Science and Engineering from TUHH, where he specialized in safety-critical real-time systems and formal methods. His research focuses on Real-Time Systems, Formal Methods including Runtime Verification and Online Model Checking, and Maximum Satisfiability problems. He contributes to developing incremental MaxSAT solvers and applies formal methods in healthcare technology, such as optimizing robotic radiation therapy systems. Education: Bachelor's degree in Computer Science and Engineering (2017), TUHH, via a dual study program in medical/safety technology. Master of Science (2020), TUHH, thesis on quantitative runtime verification for medical devices. His work includes advancing MaxSAT solver technology, as seen in publications like IGMaxHS and the noSAT-MaxSAT series. He also explores educational tools for functional programming exams, leveraging automated grading systems. His research bridges formal methods with practical applications in real-time and embedded systems. Teaching includes Functional Programming and Compiler Construction courses.

Ljiljana Brankovicمشاهده پروفایل

استاد

• Cybersecurity and Privacy
• Algorithms
• Graph Theory
• +۱ مورد دیگر

Dr. Ljiljana Brankovic is a Professor in Computational Science at the School of Science and Technology, University of New England (UNE). She holds a PhD in Computer Science from the University of Newcastle (1998) and a Graduate Electrical Engineer degree from the University of Belgrade (1987). Her research spans cybersecurity and privacy , algorithms , graph theory , and privacy-preserving machine learning , with significant funding from the Australian Research Council, German Research Foundation, London Mathematical Society, and Royal Society, UK. Education : PhD (Computer Science), University of Newcastle (1998); Graduate Electrical Engineer, University of Belgrade (1987). Her work on privacy integrates technological , ethical , legal , and psychological aspects . In algorithms, she focuses on parameterized approximation algorithms for NP-hard problems. Google Scholar articles (2025–2017) reflect her expertise in combinatorial algorithms , graph theory , privacy-preserving techniques , and machine learning applications in domains like healthcare , explosion prediction , and botanical identification . 2025 : Diverging Assessment: A Student Perspective; Advancements in Medicinal Plant Identification Using Deep Learning. 2024 : Diverging assessments: what, why, and experiences; Incremental and Zero-Shot Machine Learning for Vietnamese Medicinal Plant Classification. 2023–2017 : Domination chain complexity, ambient intelligence for health, upper domination facets, face-antimagic graphs. While no explicit awards are listed, her research funding and editorial roles (e.g., special issues on combinatorial algorithms and privacy) underscore her contributions. She has also co-authored works on statistical database security , mobile agent security , and network privacy , with over 150 publications in theoretical and applied domains.

Mehmet Emreمشاهده پروفایل

استادیار

• Programming Languages
• Software Engineering
• Program Analysis
• +۳ مورد دیگر

Mehmet Emre is an Assistant Professor in the Department of Computer Science at the University of San Francisco, where he focuses on programming languages and software engineering. He earned his PhD and MS in Computer Science from UC Santa Barbara (2022, 2021) and a BS in Computer Engineering from Boğaziçi University (2015). PhD, Computer Science, UC Santa Barbara (2022) MS, Computer Science, UC Santa Barbara (2021) BS, Computer Engineering, Boğaziçi University (2015) His research centers on program analysis with a focus on memory and thread safety, formal verification, and compiler design. Key areas include: Automated reasoning about memory safety in C programs Formalizing theoretical computer science in Lean Efficient parsing algorithms Programming language tooling for education His publications reflect work on C-to-Rust translation safety, cross-language clone detection, JavaScript analysis optimization, and compiler-related education tools. Research trends emphasize bridging formal verification with practical software transitions and improving program analysis efficiency. He directs the PL Lab at USF and coordinates the CS Tutoring Center, supporting students in courses like Programming Languages (CS 345) and Automata Theory (CS 411).

• Economic Policy
• Applied Economics
• Innovation Policy
• +۳ مورد دیگر

Elena Cefis is a Professor at the University of Bergamo , affiliated with the Department of Economics . Her work focuses on economic policy , applied economics , and digital transformation , with a strong emphasis on firm-level innovation dynamics and survival analysis. Her research explores innovation policies , technological complexity , and firm productivity , particularly in the context of national strategies like Italy’s Industry 4.0 Plan. Recent publications analyze innovation failure and its learning outcomes, patenting behavior among global R&D leaders, and exit strategies during economic cycles. She supervises academic projects, requiring students to engage in rigorous literature reviews and structured thesis development via Microsoft Teams consultations. Her teaching portfolio includes courses on Digital Transformation , International Management , and Economic Policies at both undergraduate and graduate levels.

• Artificial Intelligence
• Machine Learning
• Deep Learning
• +۵ مورد دیگر

Dr. Mohammed Oualid ATTAOUI is a Postdoctoral Researcher at the Interdisciplinary Centre for Security, Reliability and Trust (SnT) within the University of Luxembourg, affiliated with the SVV department. His work focuses on advancing artificial intelligence and machine learning methodologies, particularly in the context of deep neural network (DNN) safety analysis, clustering algorithms for data streams, and medical image processing applications. He holds a strong research interest in generative adversarial networks (GANs), black-box model analysis, and transfer learning techniques. His current position involves developing robust frameworks to ensure safety and reliability in AI systems, with notable contributions to the SAFE methodology for DNN retraining and validation. Research interests span across multiple domains including: Deep Learning and Safety-Critical AI Systems Data Stream Mining and Real-Time Clustering Medical Imaging Analysis (Histopathology, Computer Vision) Feature Selection and Optimization in Bioinformatics Subspace and Topological Clustering Approaches His recent research trends emphasize the intersection of adversarial machine learning and safety engineering, with publications demonstrating advancements in GAN-enhanced simulation-driven testing and clustering-based safety analysis for DNNs. His work bridges theoretical algorithm development with practical applications in healthcare imaging and autonomous systems. Dr. ATTAOUI's research outputs reflect a strong focus on methodological innovation with over 15 peer-reviewed articles since 2018. He actively contributes to open-source tools for data stream clustering and maintains an active presence in AI safety communities. His current projects explore adaptive clustering techniques for evolving data streams and robustness certification of medical image processing systems.

Jessica Huberمشاهده پروفایل

استاد

• Speech and voice disorders
• Parkinson's disease
• Aging speech production
• +۲ مورد دیگر

Jessica Huber is Professor and Chair of Communicative Disorders and Sciences at University at Buffalo. Researches speech interventions for Parkinson's disease and aging populations, inventing the SpeechVive device to treat communication impairments. NIH-funded program examines physiological mechanisms of speech production and technology-enhanced treatments. Fellow of National Academy of Inventors and American Speech-Language-Hearing Association. Teaches anatomy/physiology of speech and motor speech disorders.

• Computational Intelligence
• Eldercare Technology
• Geospatial Intelligence
• +۲ مورد دیگر

James Keller is a Curators Professor and R. L. Tatum Professor in the Electrical Engineering and Computer Science Department at the University of Missouri. His research in computational intelligence develops innovative applications across diverse domains including eldercare technology, bioinformatics, geospatial intelligence, and landmine detection. Keller's recent work focuses on explainable AI systems, autonomous drone navigation, advanced materials characterization, and biomedical signal processing. His laboratory employs cutting-edge techniques in deep learning, fuzzy logic, and computer vision to solve complex real-world problems. The research portfolio demonstrates strong interdisciplinary collaboration across engineering, materials science, and healthcare domains.

Samuel Ottenمشاهده پروفایل

استاد

• Mathematics Education
• Classroom Discourse
• Secondary Mathematics Instruction
• +۳ مورد دیگر

Samuel Otten is a full professor and Lois Knowles Faculty Fellow at the University of Missouri's College of Education & Human Development, serving as Department Chair for Learning, Teaching & Curriculum. He holds a Ph.D. and M.S. in Mathematics Education and Mathematics from Michigan State University, with additional degrees from Grand Valley State University. His research focuses on secondary mathematics students' participation in mathematical practices, classroom discourse, and flipped instruction. He co-leads the Flipped Mathematics Study and hosts the Math Ed Podcast, interviewing scholars in mathematics education. Education: Ph.D. and M.S. in Mathematics Education from Michigan State University; B.S. in Mathematics and Mathematics Education from Grand Valley State University. His research projects include the Flipped Mathematics Study and investigations into Common Core standards for mathematical practice. He has authored books like Missing Letters: An Alphabet Book and contributed to professional development curricula such as Connecting the NCTM Process Standards and the CCSSM Practices . Research Interests: Secondary mathematics classrooms, student engagement in reasoning and precision, flipped instruction, and professional development for teachers. He coordinates the online Master's program in Mathematics Education and co-organized PME-NA 2019. His work emphasizes incremental teacher development and the role of discourse in learning. Awards and Recognition: Not explicitly listed, but his leadership roles (e.g., Department Chair, Faculty Fellow) and prolific publications indicate significant scholarly impact. He collaborates with institutions like the NSF and the ReSTEM Institute. Grants and Leadership: Principal Investigator on NSF-funded projects and University of Missouri grants. His work spans curriculum materials analysis, teacher education, and equity in mathematics instruction. He advises on professional development frameworks like Practice-Driven PD. Outreach: Hosts the Math Ed Podcast, featuring over 300 episodes with experts in mathematics education. Coordinates outreach initiatives like the online Master's program and the Ed Specialist program. Engages in K-12 teacher training and curriculum development.

Mark Maloofمشاهده پروفایل

استاد

• Machine Learning
• Data Mining
• Online Learning Algorithms
• +۳ مورد دیگر

Mark Maloof is a Professor in the Department of Computer Science at Georgetown University, where he previously served as Director of Undergraduate Studies and pioneered the institution's graduate computer science programs. His research focuses on machine learning, data mining, online learning algorithms, and concept drift with applications in computer security. Core innovations include Dynamic Weighted Majority (an ensemble method for drifting concepts), Bayesian approaches to concept drift, and systems for detecting malicious executables and insider threats. Significant contributions include developing paired learner architectures for concept drift, additive expert ensembles, and machine learning systems for rooftop detection in aerial imagery. Security applications feature prominently, including novel methods for malware detection and systems for identifying trade secret violations.

Pingfan Wangمشاهده پروفایل

مدرس

• Artificial Intelligence
• Data Mining
• Machine Learning
• +۶ مورد دیگر

Pingfan Wang is a Lecturer in the Department of Computer and Information Sciences at Northumbria University. His research focuses on data-driven methodologies, including concept drift detection in data streams, applications of AI in environmental sustainability, and analysis of terrorism incidents using clustering techniques. He has contributed to several notable publications in these areas. Research interests include artificial intelligence, data mining, machine learning, environmental sustainability, and security analytics. His work often integrates advanced statistical models with real-world applications in public policy and energy security. Pingfan has published extensively on topics such as concept drift detection, explainable AI, and tourism review mining. His recent work includes studies on the impact of Lung-Strengthening Qigong on wellbeing, demonstrating interdisciplinary research interests. No scientific awards or grants are listed in the provided information.

Justin Ruthsمشاهده پروفایل

دانشیار

• Control Systems
• Cyber-Physical Systems Security
• Neuroscience
• +۵ مورد دیگر

Justin Ruths is an Associate Professor at the University of Texas at Dallas, holding appointments in the Mechanical Engineering and Systems Engineering departments at the Erik Jonsson School of Engineering and Computer Science. He earned his PhD in Systems Science and Applied Mathematics from Washington University in St. Louis (2011), with prior degrees from Columbia University (MS Electrical Engineering), Rice University (BS Physics), and additional MS in Mechanical Engineering from Columbia. His research focuses on control & optimization of complex dynamical systems, cyber-physical system security, neuroscience, and quantum control. Notable projects include the Applied Systems Lab, developing the zonoLAB MATLAB toolbox, and leading the Nova team for autonomous vehicle software. His work bridges theoretical control systems with practical applications in robotics safety, secure industrial control, and neural network analysis. Ruths teaches MECH 4310 (Systems & Control) and SYSM 6302 (Dynamics of Complex Networks & Systems), with over 42k views on his YouTube lecture series. His recent publications emphasize cybersecurity in control systems, neural network verification, and resilient co-design frameworks. He advises graduate students through the RIDE program and undergraduate researchers via Nova's open-door policy. His research tools include hybrid zonotopes for set-based analysis, emphasizing formal verification and anomaly detection in cyber-physical systems. Current interests span attack-resistant control architectures, quantum control algorithms, and interdisciplinary applications of network dynamics.